-------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\mhoro\Dropbox\Automation Bias Survey Data\HorowitzKahnAutomationBiasReplication.l
> og
  log type:  text
 opened on:  14 Jan 2024, 14:43:52

. 
. insheet using "reshaped_automationBias_dataComplete_withIndices.csv", comma
(161 vars, 90,160 obs)

. 
. * Corrections from data being in R *
. gen level2=.
(90,160 missing values generated)

. replace level2=0 if level=="level3_test1" | level=="level3_test2" | level=="level3_test3" | level=="l
> evel3_test4" | level=="level3_test5"
(45,080 real changes made)

. replace level2=1 if level=="level4_test1" | level=="level4_test2" | level=="level4_test3" | level=="l
> evel4_test4" | level=="level4_test5"
(45,080 real changes made)

. label var level2 "Difficulty: 0 if Level3, 1 if Level 4"

. rename level level_text

. rename level2 level

. 
. replace confirmation_correct="." if confirmation_correct=="NA"
(10,002 real changes made)

. replace qpolitical="." if qpolitical=="NA"
(10,000 real changes made)

. replace qpolitical="." if qpolitical=="Don’t know"
(0 real changes made)

. replace switched_ai="." if switched_ai=="NA"
(10,002 real changes made)

. replace switched_human="." if switched_human=="NA"
(10,002 real changes made)

. * replace ideo10 ="0" if ideo10=="0 - Left"
. * replace ideo10 ="10" if ideo10=="10 - Right"
. * replace ideo10 ="." if ideo10=="Don't know"
. * replace ideo10="." if ideo10=="NA"
. replace overall_aibeliefs2="." if overall_aibeliefs2=="NA"
(16,170 real changes made)

. replace normalized_overall_aibeliefs2="." if normalized_overall_aibeliefs2=="NA"
(16,170 real changes made)

. 
. * Education *
. gen educ="."

. replace educ="0" if educationq=="I have no formal qualifications"
(13,710 real changes made)

. replace educ="1" if educationq=="I have some qualifications but did not attend university"
(22,250 real changes made)

. replace educ="2" if educationq=="I attended university but did not graduate"
(8,720 real changes made)

. replace educ="3" if educationq=="I attended university and achieved a Bachelor’s degree (e.g. BA, BSc
> , etc.)"
(5,180 real changes made)

. replace educ="3" if educationq=="I attended university and achieved a Bachelor's degree (for example 
> BA, BSc, etc.)"
(26,400 real changes made)

. replace educ="4" if educationq=="I attended graduate school but did not graduate"
(780 real changes made)

. replace educ="4" if educationq=="I attended university and began a graduate programme, but did not co
> mplete that programme"
(1,100 real changes made)

. replace educ="5" if educationq=="I attended graduate school and went to achieve a higher degree (e.g.
>  MA, MSc, PhD, Mphil, etc.)"
(3,850 real changes made)

. replace educ="5" if educationq=="I attended university and went on to achieve a higher degree (for ex
> ample MA, MSc, PhD, Mphil, etc.)"
(8,170 real changes made)

. 
. replace overall_aisentiment_index="." if overall_aisentiment_index=="NA"
(16,170 real changes made)

. 
. destring, replace
v1 already numeric; no replace
caseid already numeric; no replace
country: contains nonnumeric characters; no replace
level_text: contains nonnumeric characters; no replace
image already numeric; no replace
treatment: contains nonnumeric characters; no replace
treatment_suggestion_correct: contains nonnumeric characters; no replace
treatment_ai already numeric; no replace
treatment_human already numeric; no replace
treatment_highconfidence already numeric; no replace
treatment_lowconfidence already numeric; no replace
identification already numeric; no replace
confirmation: contains nonnumeric characters; no replace
identification_correct already numeric; no replace
confirmation_correct: all characters numeric; replaced as byte
(10002 missing values generated)
switched: contains nonnumeric characters; no replace
switched_ai: all characters numeric; replaced as byte
(10002 missing values generated)
switched_human: all characters numeric; replaced as byte
(10002 missing values generated)
switch_direction: contains nonnumeric characters; no replace
level3_attention_check: contains nonnumeric characters; no replace
level3_attention_check_correct already numeric; no replace
lvl3_pre_treatment_correct already numeric; no replace
lvl3_pre_treatment_accuracy already numeric; no replace
lvl3_post_treatment_correct already numeric; no replace
lvl3_count_noncontrol_treatments already numeric; no replace
lvl3_post_treatment_accuracy already numeric; no replace
lvl3_times_switchedai already numeric; no replace
lvl3_times_switchedhuman already numeric; no replace
lvl3_times_switched already numeric; no replace
level4_attention_check: contains nonnumeric characters; no replace
level4_attention_check_correct already numeric; no replace
lvl4_pre_treatment_correct already numeric; no replace
lvl4_pre_treatment_accuracy already numeric; no replace
lvl4_post_treatment_correct already numeric; no replace
lvl4_count_noncontrol_treatments already numeric; no replace
lvl4_post_treatment_accuracy already numeric; no replace
lvl4_times_switchedai already numeric; no replace
lvl4_times_switchedhuman already numeric; no replace
lvl4_times_switched already numeric; no replace
overall_pre_treatment_accuracy already numeric; no replace
overall_post_treatment_accuracy already numeric; no replace
overall_times_switchedai already numeric; no replace
overall_times_switchedhuman already numeric; no replace
overall_times_switched already numeric; no replace
total_treatment_correct already numeric; no replace
overall_treatment_accuracy already numeric; no replace
practice_test1_image already numeric; no replace
practice_test1_identify already numeric; no replace
practice_test1_correct already numeric; no replace
practice_test2_image already numeric; no replace
practice_test2_identify already numeric; no replace
practice_test2_correct already numeric; no replace
practice_test3_image already numeric; no replace
practice_test3_identify already numeric; no replace
practice_test3_correct already numeric; no replace
practice_test4_image already numeric; no replace
practice_test4_identify already numeric; no replace
practice_test4_correct already numeric; no replace
practice_test5_image already numeric; no replace
practice_test5_identify already numeric; no replace
practice_test5_correct already numeric; no replace
total_practice_correct already numeric; no replace
percentage_practice_correct already numeric; no replace
quizq1_1 already numeric; no replace
quizq1_2 already numeric; no replace
quizq1_3 already numeric; no replace
quizq1_5 already numeric; no replace
quizq1_6 already numeric; no replace
quizq1_score already numeric; no replace
quizq1_score2 already numeric; no replace
quizq2_1 already numeric; no replace
quizq2_2 already numeric; no replace
quizq2_3 already numeric; no replace
quizq2_5 already numeric; no replace
quizq2_7 already numeric; no replace
quizq2_8 already numeric; no replace
quizq2_score already numeric; no replace
quizq2_score2 already numeric; no replace
familiarityaiq_1 already numeric; no replace
familiarityaiq_2 already numeric; no replace
familiarityaiq_3 already numeric; no replace
familiarityaiq_4 already numeric; no replace
familiarityaiq_5 already numeric; no replace
familiarityaiq_6 already numeric; no replace
level_familiarityai already numeric; no replace
programmingexperienceq: contains nonnumeric characters; no replace
programmingexperience_level already numeric; no replace
applicationsuseq_1: contains nonnumeric characters; no replace
applicationsuseq_2: contains nonnumeric characters; no replace
applicationsuseq_3: contains nonnumeric characters; no replace
applicationsuseq_4: contains nonnumeric characters; no replace
applicationsuseq_5: contains nonnumeric characters; no replace
applicationsuseq_6: contains nonnumeric characters; no replace
applicationsuseq_7: contains nonnumeric characters; no replace
applicationsuseq_8: contains nonnumeric characters; no replace
applicationsuseq_9: contains nonnumeric characters; no replace
potentialaiq_1: contains nonnumeric characters; no replace
potentialaiq_2: contains nonnumeric characters; no replace
potentialaiq_3: contains nonnumeric characters; no replace
potentialaiq_4: contains nonnumeric characters; no replace
potentialaiq_5: contains nonnumeric characters; no replace
potentialaiq_6: contains nonnumeric characters; no replace
potentialaiq_7: contains nonnumeric characters; no replace
potentialaiq_8: contains nonnumeric characters; no replace
potentialaiq_9: contains nonnumeric characters; no replace
overall_percieved_use_ai_apps already numeric; no replace
aibeliefsq_a: contains nonnumeric characters; no replace
aibeliefsq_b: contains nonnumeric characters; no replace
aibeliefsq_c: contains nonnumeric characters; no replace
aibeliefsq_d: contains nonnumeric characters; no replace
aibeliefsq_e: contains nonnumeric characters; no replace
aibeliefsq_f: contains nonnumeric characters; no replace
aibeliefsq_g: contains nonnumeric characters; no replace
positive_aibeliefs_index: contains nonnumeric characters; no replace
negative_aibeliefs_index: contains nonnumeric characters; no replace
overall_aisentiment_index: all characters numeric; replaced as double
(16170 missing values generated)
overall_aibeliefs2: all characters numeric; replaced as byte
(16170 missing values generated)
normalized_overall_aibeliefs2: all characters numeric; replaced as double
(16170 missing values generated)
overallai_experience already numeric; no replace
overallai_experience2 already numeric; no replace
overallai_familiarity already numeric; no replace
overallai_knowledge already numeric; no replace
overallai_knowledge2 already numeric; no replace
ai_background_index already numeric; no replace
ai_background_index2 already numeric; no replace
ai_background_index3 already numeric; no replace
educationq: contains nonnumeric characters; no replace
degree_1: contains nonnumeric characters; no replace
degree_2: contains nonnumeric characters; no replace
degree_3: contains nonnumeric characters; no replace
degree_4: contains nonnumeric characters; no replace
degree_5: contains nonnumeric characters; no replace
degree_6: contains nonnumeric characters; no replace
degree_7: contains nonnumeric characters; no replace
degree_8: contains nonnumeric characters; no replace
degree_9: contains nonnumeric characters; no replace
educationinstem_level already numeric; no replace
sex: contains nonnumeric characters; no replace
age already numeric; no replace
birthyr: contains nonnumeric characters; no replace
ethnicity: contains nonnumeric characters; no replace
household_income: contains nonnumeric characters; no replace
qpolitical: all characters numeric; replaced as int
(10000 missing values generated)
pid3: contains nonnumeric characters; no replace
pid7: contains nonnumeric characters; no replace
marstat: contains nonnumeric characters; no replace
child18: contains nonnumeric characters; no replace
employ: contains nonnumeric characters; no replace
inputstate: contains nonnumeric characters; no replace
immigrant: contains nonnumeric characters; no replace
votereg: contains nonnumeric characters; no replace
turnout20post: contains nonnumeric characters; no replace
presvote20post: contains nonnumeric characters; no replace
newsint: contains nonnumeric characters; no replace
presvote16post: contains nonnumeric characters; no replace
pew_bornagain: contains nonnumeric characters; no replace
pew_religimp: contains nonnumeric characters; no replace
pew_churatd: contains nonnumeric characters; no replace
pew_prayer: contains nonnumeric characters; no replace
religpew: contains nonnumeric characters; no replace
weight already numeric; no replace
level already numeric; no replace
educ: all characters numeric; replaced as byte

. 
. * replace qpolitical=ideo10 if ideo10>=0 & ideo10<=10 & qpolitical==.
. replace qpolitical=. if qpolitical==999
(9,940 real changes made, 9,940 to missing)

. 
. gen switch=0

. replace switch=1 if switched_ai==1 | switched_human==1
(18,084 real changes made)

. label var switch "Respondent Switched In Response To Treatment"

. 
. gen quiz=0

. replace quiz=1 if quizq1_score==1 | quizq2_score==1
(18,320 real changes made)

. replace quiz=2 if quizq1_score==1 & quizq2_score==1
(250 real changes made)

. label var quiz "AI Quiz 1 = one right, 2 = both right"

. 
. gen female=0

. replace female=1 if sex=="Female"
(46,280 real changes made)

. 
. gen treatment_control=0

. replace treatment_control=1 if treatment_human==0 & treatment_ai==0
(10,002 real changes made)

. 
. egen newid = group(caseid)

. 
. gen australia=0

. replace australia=1 if country=="Australia"
(10,030 real changes made)

. gen china=0

. replace china=1 if country=="China"
(10,080 real changes made)

. gen france=0

. replace france=1 if country=="France"
(10,020 real changes made)

. gen japan=0

. replace japan=1 if country=="Japan"
(10,010 real changes made)

. gen skorea=0

. replace skorea=1 if country=="Republic of Korea"
(10,010 real changes made)

. gen russia=0

. replace russia=1 if country=="Russian Federation"
(10,000 real changes made)

. gen sweden=0

. replace sweden=1 if country=="Sweden"
(10,010 real changes made)

. gen usa=0

. replace usa=1 if country=="United States"
(10,000 real changes made)

. gen uk=0

. replace uk=1 if country=="United Kingdom"
(10,000 real changes made)

. 
. gen countryid=0

. replace countryid=1 if country=="Australia"
(10,030 real changes made)

. replace countryid=2 if country=="China"
(10,080 real changes made)

. replace countryid=3 if country=="France"
(10,020 real changes made)

. replace countryid=4 if country=="Japan"
(10,010 real changes made)

. replace countryid=5 if country=="Republic of Korea"
(10,010 real changes made)

. replace countryid=6 if country=="Russian Federation"
(10,000 real changes made)

. replace countryid=7 if country=="Sweden"
(10,010 real changes made)

. replace countryid=8 if country=="United States"
(10,000 real changes made)

. replace countryid=9 if country=="United Kingdom"
(10,000 real changes made)

. 
. label var overallai_familiarity "AI Familiarity"

. label var overall_aisentiment_index "Overall AI Sentiment Index"

. label var level "Level of Difficulty"

. label var total_practice_correct "Practice Round Accuracy"

. label var age "Age"

. label var female "Gender"

. label var educ "Level of Education"

. label var treatment_high "Treatment Condition: High Confidence"

. label var ai_background_index3 "AI Background Index"

. label var russia "Russia"

. label var china "China"

. label var sweden "Sweden"

. label var uk "United Kingdom"

. label var usa "United States"

. label var france "France"

. label var japan "Japan"

. label var australia "Australia"

. label var skorea "South Korea"

. 
. gen switchright=.
(90,160 missing values generated)

. replace switchright=1 if identification_correct==0 & confirmation_correct ==1
(8,535 real changes made)

. replace switchright=0 if identification_correct==0 & confirmation_correct ==0
(26,122 real changes made)

. replace switchright=0 if identification_correct==1 & confirmation_correct ==0
(9,549 real changes made)

. replace switchright=0 if identification_correct==1 & confirmation_correct ==1
(35,952 real changes made)

. gen switchwrong=.
(90,160 missing values generated)

. replace switchwrong=1 if identification_correct==1 & confirmation_correct == 0
(9,549 real changes made)

. replace switchwrong=0 if identification_correct==1 & confirmation_correct == 1
(35,952 real changes made)

. replace switchwrong=0 if identification_correct==0 & confirmation_correct == 1
(8,535 real changes made)

. replace switchwrong=0 if identification_correct==0 & confirmation_correct == 0
(26,122 real changes made)

. 
. gen aifamiliaritysquared=overallai_familiarity*overallai_familiarity

. label var aifamiliaritysquared "AI Familiarity Squared"

. 
. label var normalized_overall_aibeliefs2 "Normalized AI Sentiment"

. 
. gen aibackground=(overallai_experience2*(.4))+(overallai_familiarity*(.4))+(overallai_knowledge2*(.2)
> )

. gen aibackgroundsquared=aibackground*aibackground

. label var aibackgroundsquared "AI Background Index Squared"

. label var aibackground "AI Background Index"

. label var treatment_ai "Treatment Condition: AI"

. label var qpolitical "Political Ideology"

. gen aiexperiencesquared=overallai_experience2*overallai_experience2

. label var aiexperiencesquared "AI Experience Squared"

. gen overallai_knowledge2squared= overallai_knowledge2*overallai_knowledge2

. label var overallai_knowledge2squared "AI Knowledge Squared"

. label var overallai_knowledge2 "AI Knowledge"

. gen overallai_experience2squared=overallai_experience2*overallai_experience2

. label var overallai_experience2squared "AI Experience Squared"

. label var overallai_experience2 "AI Experience"

. 
. * Table 2
. 
. * Overall Binary
. eststo m1: regress switch level total_practice_correct age female educ treatment_ai treatment_high ru
> ssia china uk australia japan skorea france sweden, cluster(caseid)

Linear regression                               Number of obs     =     90,160
                                                F(15, 9001)       =      88.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0202
                                                Root MSE          =      .3964

                                         (Std. Err. adjusted for 9,002 clusters in caseid)
------------------------------------------------------------------------------------------
                         |               Robust
                  switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   level |   .0337096   .0025719    13.11   0.000     .0286681    .0387511
  total_practice_correct |  -.0081521   .0017856    -4.57   0.000    -.0116522    -.004652
                     age |  -.0002468   .0001267    -1.95   0.052    -.0004953    1.64e-06
                  female |   .0304819   .0041808     7.29   0.000     .0222866    .0386771
                    educ |   .0049433   .0013723     3.60   0.000     .0022533    .0076334
            treatment_ai |   .0448134   .0026537    16.89   0.000     .0396115    .0500152
treatment_highconfidence |   .0557917   .0026517    21.04   0.000     .0505938    .0609897
                  russia |  -.0377135   .0083104    -4.54   0.000    -.0540038   -.0214232
                   china |    .070338   .0096837     7.26   0.000     .0513557    .0893203
                      uk |   .0236763   .0088381     2.68   0.007     .0063517    .0410009
               australia |   .0184411   .0087391     2.11   0.035     .0013105    .0355717
                   japan |    -.03199    .008538    -3.75   0.000    -.0487263   -.0152537
                  skorea |  -.0402599   .0087698    -4.59   0.000    -.0574507    -.023069
                  france |  -.0114725   .0087934    -1.30   0.192    -.0287096    .0057646
                  sweden |  -.0052285   .0085704    -0.61   0.542    -.0220284    .0115714
                   _cons |    .146926   .0112157    13.10   0.000     .1249406    .1689114
------------------------------------------------------------------------------------------

. 
. * Overall AI Condition
. eststo m2: regress switch level total_practice_correct age female educ treatment_highconfidence russi
> a china uk australia japan skorea france sweden if treatment_control==0 & treatment_ai==1, cluster(ca
> seid)

Linear regression                               Number of obs     =     39,903
                                                F(14, 8990)       =      26.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0147
                                                Root MSE          =     .41672

                                         (Std. Err. adjusted for 8,991 clusters in caseid)
------------------------------------------------------------------------------------------
                         |               Robust
                  switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   level |   .0352423   .0040495     8.70   0.000     .0273043    .0431803
  total_practice_correct |  -.0112749   .0023692    -4.76   0.000     -.015919   -.0066307
                     age |  -.0003551   .0001674    -2.12   0.034    -.0006832    -.000027
                  female |   .0347569   .0055453     6.27   0.000     .0238868    .0456269
                    educ |   .0067082   .0018004     3.73   0.000      .003179    .0102374
treatment_highconfidence |   .0169665     .00396     4.28   0.000      .009204     .024729
                  russia |  -.0518279   .0111836    -4.63   0.000    -.0737504   -.0299054
                   china |   .0808338   .0129478     6.24   0.000     .0554532    .1062144
                      uk |   .0236333   .0116114     2.04   0.042     .0008723    .0463943
               australia |    .017044   .0117784     1.45   0.148    -.0060444    .0401324
                   japan |  -.0324013     .01152    -2.81   0.005    -.0549831   -.0098195
                  skorea |  -.0455338   .0117931    -3.86   0.000     -.068651   -.0224166
                  france |  -.0155122   .0117836    -1.32   0.188    -.0386108    .0075864
                  sweden |  -.0074948    .011558    -0.65   0.517    -.0301512    .0151615
                   _cons |   .2193854   .0149219    14.70   0.000      .190135    .2486358
------------------------------------------------------------------------------------------

. 
. * Overall Human Condition
. eststo m3: regress switch level total_practice_correct age female educ treatment_highconfidence russi
> a china uk australia japan skorea france sweden if treatment_control==0 & treatment_human==1, cluster
> (caseid)

Linear regression                               Number of obs     =     40,255
                                                F(14, 8994)       =      27.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0137
                                                Root MSE          =     .41343

                                         (Std. Err. adjusted for 8,995 clusters in caseid)
------------------------------------------------------------------------------------------
                         |               Robust
                  switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   level |   .0408757   .0039411    10.37   0.000     .0331502    .0486013
  total_practice_correct |  -.0075562   .0023205    -3.26   0.001    -.0121048   -.0030075
                     age |  -.0002011   .0001644    -1.22   0.221    -.0005233    .0001211
                  female |   .0341693    .005418     6.31   0.000     .0235488    .0447897
                    educ |   .0045873   .0018034     2.54   0.011     .0010523    .0081223
treatment_highconfidence |   .0161031   .0040136     4.01   0.000     .0082355    .0239708
                  russia |  -.0351321   .0112977    -3.11   0.002    -.0572783    -.012986
                   china |   .0773231   .0125382     6.17   0.000     .0527453    .1019009
                      uk |   .0284807   .0118214     2.41   0.016     .0053081    .0516532
               australia |   .0247962   .0117421     2.11   0.035      .001779    .0478133
                   japan |  -.0403183   .0111746    -3.61   0.000     -.062223   -.0184136
                  skorea |  -.0451896   .0114587    -3.94   0.000    -.0676514   -.0227279
                  france |  -.0097989   .0116647    -0.84   0.401    -.0326643    .0130665
                  sweden |  -.0063065    .011363    -0.56   0.579    -.0285805    .0159676
                   _cons |   .1964657   .0149161    13.17   0.000     .1672267    .2257047
------------------------------------------------------------------------------------------

. 
. *AI Familiarity Squared
. eststo m4: logit switch qpolitical level total_practice_correct age female educ treatment_highconfide
> nce overallai_familiarity aifamiliaritysquared normalized_overall_aibeliefs2 [pweight=weight] if trea
> tment_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14203.176  
Iteration 2:   log pseudolikelihood =  -14202.63  
Iteration 3:   log pseudolikelihood =  -14202.63  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =     115.84
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -14202.63               Pseudo R2         =     0.0095

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0323593   .0095913     3.37   0.001     .0135607    .0511579
                        level |    .200319   .0339329     5.90   0.000     .1338118    .2668262
       total_practice_correct |  -.0419898   .0191777    -2.19   0.029    -.0795774   -.0044022
                          age |  -.0004254   .0016155    -0.26   0.792    -.0035917     .002741
                       female |   .2248604   .0488918     4.60   0.000     .1290342    .3206866
                         educ |   .0251092    .015111     1.66   0.097    -.0045078    .0547261
     treatment_highconfidence |   .0926448   .0341502     2.71   0.007     .0257116    .1595781
        overallai_familiarity |   1.928334   .3881926     4.97   0.000     1.167491    2.689178
         aifamiliaritysquared |  -1.788319   .4630048    -3.86   0.000    -2.695792   -.8808463
normalized_overall_aibeliefs2 |  -.3493811   .1581354    -2.21   0.027    -.6593207   -.0394415
                        _cons |  -1.587943   .1671813    -9.50   0.000    -1.915612   -1.260274
-----------------------------------------------------------------------------------------------

. 
. * AI Knowledge Squared
. eststo m5: logit switch qpolitical level total_practice_correct age female educ treatment_high overal
> lai_knowledge2 overallai_knowledge2squared normalized_overall_aibeliefs2 [pweight=weight] if treatmen
> t_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14237.593  
Iteration 2:   log pseudolikelihood = -14237.277  
Iteration 3:   log pseudolikelihood = -14237.277  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =     100.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14237.277               Pseudo R2         =     0.0071

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0365165   .0098041     3.72   0.000     .0173008    .0557321
                        level |   .1998434   .0338297     5.91   0.000     .1335385    .2661483
       total_practice_correct |  -.0465388   .0193247    -2.41   0.016    -.0844144   -.0086631
                          age |   -.000902   .0016522    -0.55   0.585    -.0041403    .0023363
                       female |    .211315   .0492515     4.29   0.000     .1147839    .3078461
                         educ |   .0425127   .0147251     2.89   0.004     .0136519    .0713734
     treatment_highconfidence |   .0921087   .0341458     2.70   0.007     .0251842    .1590333
         overallai_knowledge2 |   .9539053   .4116742     2.32   0.020     .1470387    1.760772
  overallai_knowledge2squared |  -1.366445   .5297904    -2.58   0.010    -2.404816   -.3280753
normalized_overall_aibeliefs2 |  -.2034334   .1585253    -1.28   0.199    -.5141373    .1072706
                        _cons |  -1.554225   .1738196    -8.94   0.000    -1.894906   -1.213545
-----------------------------------------------------------------------------------------------

. 
. * AI Experience Squared
. eststo m6: logit switch qpolitical level total_practice_correct age female educ treatment_high overal
> lai_experience2 overallai_experience2squared normalized_overall_aibeliefs2 [pweight=weight] if treatm
> ent_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14230.013  
Iteration 2:   log pseudolikelihood =  -14229.66  
Iteration 3:   log pseudolikelihood =  -14229.66  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =     105.74
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -14229.66               Pseudo R2         =     0.0076

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |    .039261   .0098712     3.98   0.000     .0199139    .0586082
                        level |   .2007747   .0338178     5.94   0.000      .134493    .2670564
       total_practice_correct |  -.0466243   .0192859    -2.42   0.016     -.084424   -.0088245
                          age |  -.0005783   .0017434    -0.33   0.740    -.0039953    .0028388
                       female |   .2060356   .0492781     4.18   0.000     .1094523    .3026189
                         educ |   .0443404   .0152784     2.90   0.004     .0143953    .0742855
     treatment_highconfidence |   .0927875   .0341702     2.72   0.007     .0258151    .1597599
        overallai_experience2 |   .9228828   .2802157     3.29   0.001     .3736701    1.472095
 overallai_experience2squared |  -1.133728   .2998895    -3.78   0.000    -1.721501   -.5459555
normalized_overall_aibeliefs2 |  -.2233259   .1601813    -1.39   0.163    -.5372756    .0906237
                        _cons |  -1.579212   .1709444    -9.24   0.000    -1.914257   -1.244168
-----------------------------------------------------------------------------------------------

. 
. *AI Background Squared
. eststo m7: logit switch qpolitical level total_practice_correct age female educ treatment_highconfide
> nce aibackground aibackgroundsquared normalized_overall_aibeliefs2 [pweight=weight] if treatment_ai==
> 1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14236.318  
Iteration 2:   log pseudolikelihood = -14236.006  
Iteration 3:   log pseudolikelihood = -14236.006  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =      98.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14236.006               Pseudo R2         =     0.0072

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0341676   .0096864     3.53   0.000     .0151826    .0531525
                        level |   .2003063   .0338309     5.92   0.000      .133999    .2666136
       total_practice_correct |  -.0444096   .0192835    -2.30   0.021    -.0822046   -.0066146
                          age |  -.0002336   .0017496    -0.13   0.894    -.0036628    .0031956
                       female |   .2177532   .0498295     4.37   0.000     .1200892    .3154173
                         educ |   .0363564   .0153473     2.37   0.018     .0062761    .0664366
     treatment_highconfidence |   .0924249   .0341472     2.71   0.007     .0254977    .1593521
                 aibackground |   1.288577   .4830002     2.67   0.008     .3419136     2.23524
          aibackgroundsquared |  -1.547171   .6532244    -2.37   0.018    -2.827468    -.266875
normalized_overall_aibeliefs2 |  -.2736115   .1610658    -1.70   0.089    -.5892946    .0420715
                        _cons |  -1.614762   .1797858    -8.98   0.000    -1.967136   -1.262388
-----------------------------------------------------------------------------------------------

. 
. esttab m1 m2 m3 m4 m5 m6 m7 using Table2.tex, replace f t(3) scalars("ll Log Likelihood" F) legend la
> bel collabels(none) varlabels(_cons Constant) se(3) pr2 r2 b(3) star(* 0.10 ** 0.05 *** 0.01) nobasel
> evels order(qpolitical level total_practice_correct age female educ treatment_ai treatment_highconfid
> ence overallai_familiarity aifamiliaritysquared overallai_knowledge2 overallai_knowledge2squared over
> allai_experience2 overallai_experience2squared aibackground aibackgroundsquared normalized_overall_ai
> beliefs2) drop(russia china uk australia japan skorea france sweden) mtitles("\shortstack{Overall\\Bi
> nary\\b/SE}" "\shortstack{AI Condition\\Binary\\b/SE}" "\shortstack{Human Condition\\Binary\\b/SE}" "
> \shortstack{Switching in AI Condition\\AI Familiarity Model\\b/SE}" "\shortstack{Switching in AI Cond
> ition\\AI Knowledge Model\\b/SE}" "\shortstack{Switching in AI Condition\\AI Experience Model\\b/SE}"
>  "\shortstack{Switching in AI Condition\\AI Background Model\\b/SE}") eqlabel(none)
(output written to Table2.tex)

. 
. esttab m1 m2 m3 m4 m5 m6 m7 using Table2.rtf, replace t(3) scalars("ll Log Likelihood" F) legend labe
> l collabels(none) varlabels(_cons Constant) se(3) pr2 r2 b(3) star(* 0.10 ** 0.05 *** 0.01) nobaselev
> els order(qpolitical level total_practice_correct age female educ treatment_ai treatment_highconfiden
> ce overallai_familiarity aifamiliaritysquared overallai_knowledge2 overallai_knowledge2squared overal
> lai_experience2 overallai_experience2squared aibackground aibackgroundsquared normalized_overall_aibe
> liefs2) drop(russia china uk australia japan skorea france sweden) mtitles("\shortstack{Overall\\Bina
> ry\\b/SE}" "\shortstack{AI Condition\\Binary\\b/SE}" "\shortstack{Human Condition\\Binary\\b/SE}" "\s
> hortstack{Switching in AI Condition\\AI Familiarity Model\\b/SE}" "\shortstack{Switching in AI Condit
> ion\\AI Knowledge Model\\b/SE}" "\shortstack{Switching in AI Condition\\AI Experience Model\\b/SE}" "
> \shortstack{Switching in AI Condition\\AI Background Model\\b/SE}") eqlabel(none)
(output written to Table2.rtf)

. 
. 
. * Figure 4
. 
. estimates clear

. 
. */ AI Familiarity */
. 
. logit switch qpolitical level total_practice_correct age female educ treatment_high c.overallai_famil
> iarity##c.overallai_familiarity normalized_overall_aibeliefs2 [pweight=weight] if treatment_ai==1, cl
> uster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14203.176  
Iteration 2:   log pseudolikelihood =  -14202.63  
Iteration 3:   log pseudolikelihood =  -14202.63  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =     115.84
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -14202.63               Pseudo R2         =     0.0095

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0323593   .0095913     3.37   0.001     .0135607    .0511579
                        level |    .200319   .0339329     5.90   0.000     .1338118    .2668262
       total_practice_correct |  -.0419898   .0191777    -2.19   0.029    -.0795774   -.0044022
                          age |  -.0004254   .0016155    -0.26   0.792    -.0035917     .002741
                       female |   .2248604   .0488918     4.60   0.000     .1290342    .3206866
                         educ |   .0251092    .015111     1.66   0.097    -.0045078    .0547261
     treatment_highconfidence |   .0926448   .0341502     2.71   0.007     .0257116    .1595781
        overallai_familiarity |   1.928334   .3881926     4.97   0.000     1.167491    2.689178
                              |
      c.overallai_familiarity#|
      c.overallai_familiarity |  -1.788319   .4630048    -3.86   0.000    -2.695792   -.8808463
                              |
normalized_overall_aibeliefs2 |  -.3493811   .1581354    -2.21   0.027    -.6593207   -.0394415
                        _cons |  -1.587943   .1671813    -9.50   0.000    -1.915612   -1.260274
-----------------------------------------------------------------------------------------------

. 
. eststo m1: margins, at( (means) _all overallai_familiarity=(0(.1)1)) post

Adjusted predictions                            Number of obs     =     26,214
Model VCE    : Robust

Expression   : Pr(switch), predict()

1._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =           0
               normalized~2    =    .5668233 (mean)

2._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .1
               normalized~2    =    .5668233 (mean)

3._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .2
               normalized~2    =    .5668233 (mean)

4._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .3
               normalized~2    =    .5668233 (mean)

5._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .4
               normalized~2    =    .5668233 (mean)

6._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .5
               normalized~2    =    .5668233 (mean)

7._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .6
               normalized~2    =    .5668233 (mean)

8._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .7
               normalized~2    =    .5668233 (mean)

9._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .8
               normalized~2    =    .5668233 (mean)

10._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =          .9
               normalized~2    =    .5668233 (mean)

11._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overallai_~y    =           1
               normalized~2    =    .5668233 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1948345    .007579    25.71   0.000     .1799798    .2096891
          2  |   .2237497   .0043828    51.05   0.000     .2151595    .2323398
          3  |   .2488484   .0056234    44.25   0.000     .2378267    .2598702
          4  |   .2686769   .0086312    31.13   0.000     .2517599    .2855938
          5  |   .2821757   .0111228    25.37   0.000     .2603754     .303976
          6  |   .2886786   .0129581    22.28   0.000     .2632812    .3140761
          7  |   .2878822   .0147317    19.54   0.000     .2590085    .3167559
          8  |    .279823   .0173462    16.13   0.000     .2458251    .3138209
          9  |   .2648805   .0214021    12.38   0.000     .2229332    .3068278
         10  |   .2438042   .0267528     9.11   0.000     .1913696    .2962388
         11  |   .2177405   .0326432     6.67   0.000     .1537611      .28172
------------------------------------------------------------------------------

. 
. coefplot m1, vertical recast(line) lcolor("240 91 67") lwidth(*2) ciopts(fcolor("254 172 129") finten
> sity(50) recast(rarea) lpatt(dash) lcolor(none)) xtitle(" " "AI Familiarity Index Squared", size(meds
> mall)) ytitle("Predicted Probability of Switching (AI Treatment)" " ", size(medsmall)) xlabel(1 "0" 2
>  ".1" 3 ".2" 4 ".3" 5 ".4" 6 ".5" 7 ".6" 8 ".7" 9 ".8" 10 ".9" 11 "1", grid) title("AI Familiarity")

. 
. graph save "aifamiliaritypredictedprobabilities.gph", replace
(file aifamiliaritypredictedprobabilities.gph saved)

. graph export "aifamiliaritypredictedprobabilities.tif", replace as(tif)
(file aifamiliaritypredictedprobabilities.tif written in TIFF format)

. 
. */ AI Knowledge */
. 
. logit switch qpolitical level total_practice_correct age female educ treatment_high c.overallai_knowl
> edge2##cc.overallai_knowledge2 normalized_overall_aibeliefs2 [pweight=weight] if treatment_ai==1, clu
> ster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14237.593  
Iteration 2:   log pseudolikelihood = -14237.277  
Iteration 3:   log pseudolikelihood = -14237.277  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =     100.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14237.277               Pseudo R2         =     0.0071

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0365165   .0098041     3.72   0.000     .0173008    .0557321
                        level |   .1998434   .0338297     5.91   0.000     .1335385    .2661483
       total_practice_correct |  -.0465388   .0193247    -2.41   0.016    -.0844144   -.0086631
                          age |   -.000902   .0016522    -0.55   0.585    -.0041403    .0023363
                       female |    .211315   .0492515     4.29   0.000     .1147839    .3078461
                         educ |   .0425127   .0147251     2.89   0.004     .0136519    .0713734
     treatment_highconfidence |   .0921087   .0341458     2.70   0.007     .0251842    .1590333
         overallai_knowledge2 |   .9539053   .4116742     2.32   0.020     .1470387    1.760772
                              |
       c.overallai_knowledge2#|
       c.overallai_knowledge2 |  -1.366445   .5297904    -2.58   0.010    -2.404816   -.3280753
                              |
normalized_overall_aibeliefs2 |  -.2034334   .1585253    -1.28   0.199    -.5141373    .1072706
                        _cons |  -1.554225   .1738196    -8.94   0.000    -1.894906   -1.213545
-----------------------------------------------------------------------------------------------

. 
. eststo m2: margins, at( (means) _all overallai_knowledge2=(0(.1)1)) post

Adjusted predictions                            Number of obs     =     26,214
Model VCE    : Robust

Expression   : Pr(switch), predict()

1._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =           0
               normalized~2    =    .5668233 (mean)

2._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .1
               normalized~2    =    .5668233 (mean)

3._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .2
               normalized~2    =    .5668233 (mean)

4._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .3
               normalized~2    =    .5668233 (mean)

5._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .4
               normalized~2    =    .5668233 (mean)

6._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .5
               normalized~2    =    .5668233 (mean)

7._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .6
               normalized~2    =    .5668233 (mean)

8._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .7
               normalized~2    =    .5668233 (mean)

9._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .8
               normalized~2    =    .5668233 (mean)

10._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =          .9
               normalized~2    =    .5668233 (mean)

11._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ge2    =           1
               normalized~2    =    .5668233 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2174484   .0099345    21.89   0.000     .1979771    .2369197
          2  |   .2316759    .005599    41.38   0.000     .2207021    .2426498
          3  |   .2414997   .0051135    47.23   0.000     .2314774     .251522
          4  |   .2464927   .0066339    37.16   0.000     .2334905    .2594948
          5  |   .2464442   .0075434    32.67   0.000     .2316595     .261229
          6  |   .2413565   .0076528    31.54   0.000     .2263573    .2563558
          7  |    .231444    .008122    28.50   0.000     .2155251    .2473629
          8  |   .2171381   .0105562    20.57   0.000     .1964484    .2378279
          9  |   .1990888   .0150654    13.22   0.000     .1695613    .2286164
         10  |   .1781541    .020621     8.64   0.000     .1377376    .2185706
         11  |   .1553636   .0261782     5.93   0.000     .1040552    .2066719
------------------------------------------------------------------------------

. 
. coefplot m2, vertical recast(line) lcolor("240 91 67") lwidth(*2) ciopts(fcolor("254 172 129") finten
> sity(50) recast(rarea) lpatt(dash) lcolor(none)) xtitle(" " "AI Knowledge Index Squared", size(medsma
> ll)) ytitle("Predicted Probability of Switching (AI Treatment)" " ", size(medsmall)) xlabel(1 "0" 2 "
> .1" 3 ".2" 4 ".3" 5 ".4" 6 ".5" 7 ".6" 8 ".7" 9 ".8" 10 ".9" 11 "1", grid) title("AI Knowledge")

. 
. graph save "aiknowledgepredictedprobabilities.gph", replace
(file aiknowledgepredictedprobabilities.gph saved)

. graph export "aiknowledgepredictedprobabilities.tif", replace as(tif)
(file aiknowledgepredictedprobabilities.tif written in TIFF format)

. 
. */ AI Experience */
. 
. logit switch qpolitical level total_practice_correct age female educ treatment_high c.overallai_exper
> ience2##cc.overallai_experience2 normalized_overall_aibeliefs2 [pweight=weight] if treatment_ai==1, c
> luster(caseid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14230.013  
Iteration 2:   log pseudolikelihood =  -14229.66  
Iteration 3:   log pseudolikelihood =  -14229.66  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =     105.74
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -14229.66               Pseudo R2         =     0.0076

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |    .039261   .0098712     3.98   0.000     .0199139    .0586082
                        level |   .2007747   .0338178     5.94   0.000      .134493    .2670564
       total_practice_correct |  -.0466243   .0192859    -2.42   0.016     -.084424   -.0088245
                          age |  -.0005783   .0017434    -0.33   0.740    -.0039953    .0028388
                       female |   .2060356   .0492781     4.18   0.000     .1094523    .3026189
                         educ |   .0443404   .0152784     2.90   0.004     .0143953    .0742855
     treatment_highconfidence |   .0927875   .0341702     2.72   0.007     .0258151    .1597599
        overallai_experience2 |   .9228828   .2802157     3.29   0.001     .3736701    1.472095
                              |
      c.overallai_experience2#|
      c.overallai_experience2 |  -1.133728   .2998895    -3.78   0.000    -1.721501   -.5459554
                              |
normalized_overall_aibeliefs2 |  -.2233259   .1601813    -1.39   0.163    -.5372756    .0906237
                        _cons |  -1.579212   .1709444    -9.24   0.000    -1.914257   -1.244168
-----------------------------------------------------------------------------------------------

. 
. eststo m3: margins, at( (means) _all overallai_experience2=(0(.1)1)) post

Adjusted predictions                            Number of obs     =     26,214
Model VCE    : Robust

Expression   : Pr(switch), predict()

1._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =           0
               normalized~2    =    .5668233 (mean)

2._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .1
               normalized~2    =    .5668233 (mean)

3._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .2
               normalized~2    =    .5668233 (mean)

4._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .3
               normalized~2    =    .5668233 (mean)

5._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .4
               normalized~2    =    .5668233 (mean)

6._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .5
               normalized~2    =    .5668233 (mean)

7._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .6
               normalized~2    =    .5668233 (mean)

8._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .7
               normalized~2    =    .5668233 (mean)

9._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .8
               normalized~2    =    .5668233 (mean)

10._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =          .9
               normalized~2    =    .5668233 (mean)

11._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               overalla~ce2    =           1
               normalized~2    =    .5668233 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .216889   .0084348    25.71   0.000      .200357     .233421
          2  |    .230953   .0056697    40.73   0.000     .2198405    .2420655
          3  |   .2414656   .0048793    49.49   0.000     .2319024    .2510287
          4  |   .2480463   .0056866    43.62   0.000     .2369007    .2591919
          5  |   .2504653   .0067318    37.21   0.000     .2372713    .2636593
          6  |   .2486399   .0074764    33.26   0.000     .2339865    .2632933
          7  |   .2426324   .0080429    30.17   0.000     .2268686    .2583962
          8  |   .2326518   .0088954    26.15   0.000     .2152171    .2500865
          9  |   .2190573   .0105087    20.85   0.000     .1984606     .239654
         10  |   .2023592   .0129875    15.58   0.000     .1769042    .2278142
         11  |   .1832122   .0160388    11.42   0.000     .1517768    .2146477
------------------------------------------------------------------------------

. 
. coefplot m3, vertical recast(line) lcolor("240 91 67") lwidth(*2) ciopts(fcolor("254 172 129") finten
> sity(50) recast(rarea) lpatt(dash) lcolor(none)) xtitle(" " "AI Experience Index Squared", size(medsm
> all)) ytitle("Predicted Probability of Switching (AI Treatment)" " ", size(medsmall)) xlabel(1 "0" 2 
> ".1" 3 ".2" 4 ".3" 5 ".4" 6 ".5" 7 ".6" 8 ".7" 9 ".8" 10 ".9" 11 "1", grid) title("AI Experience")

. 
. graph save "aiexperiencepredictedprobabilities.gph", replace
(file aiexperiencepredictedprobabilities.gph saved)

. graph export "aiexperiencepredictedprobabilities.tif", replace as(tif)
(file aiexperiencepredictedprobabilities.tif written in TIFF format)

. 
. */ AI Background */
. 
. logit switch qpolitical level total_practice_correct age female educ treatment_highconfidence c.aibac
> kground##c.aibackground normalized_overall_aibeliefs2 [pweight=weight] if treatment_ai==1, cluster(ca
> seid)

Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14236.318  
Iteration 2:   log pseudolikelihood = -14236.006  
Iteration 3:   log pseudolikelihood = -14236.006  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(10)     =      98.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14236.006               Pseudo R2         =     0.0072

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0341676   .0096864     3.53   0.000     .0151826    .0531525
                        level |   .2003063   .0338309     5.92   0.000      .133999    .2666136
       total_practice_correct |  -.0444096   .0192835    -2.30   0.021    -.0822046   -.0066146
                          age |  -.0002336   .0017496    -0.13   0.894    -.0036628    .0031956
                       female |   .2177532   .0498295     4.37   0.000     .1200892    .3154173
                         educ |   .0363564   .0153473     2.37   0.018     .0062761    .0664366
     treatment_highconfidence |   .0924249   .0341472     2.71   0.007     .0254977    .1593521
                 aibackground |   1.288577   .4830002     2.67   0.008     .3419136     2.23524
                              |
c.aibackground#c.aibackground |  -1.547171   .6532244    -2.37   0.018    -2.827468   -.2668751
                              |
normalized_overall_aibeliefs2 |  -.2736115   .1610658    -1.70   0.089    -.5892946    .0420715
                        _cons |  -1.614762   .1797858    -8.98   0.000    -1.967136   -1.262388
-----------------------------------------------------------------------------------------------

. 
. eststo m4: margins, at( (means) _all aibackground=(0(.1)1)) post

Adjusted predictions                            Number of obs     =     26,214
Model VCE    : Robust

Expression   : Pr(switch), predict()

1._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =           0
               normalized~2    =    .5668233 (mean)

2._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .1
               normalized~2    =    .5668233 (mean)

3._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .2
               normalized~2    =    .5668233 (mean)

4._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .3
               normalized~2    =    .5668233 (mean)

5._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .4
               normalized~2    =    .5668233 (mean)

6._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .5
               normalized~2    =    .5668233 (mean)

7._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .6
               normalized~2    =    .5668233 (mean)

8._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .7
               normalized~2    =    .5668233 (mean)

9._at        : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .8
               normalized~2    =    .5668233 (mean)

10._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =          .9
               normalized~2    =    .5668233 (mean)

11._at       : qpolitical      =    5.660474 (mean)
               level           =    .4994502 (mean)
               total_prac~t    =    2.557174 (mean)
               age             =    47.10679 (mean)
               female          =    .4807136 (mean)
               educ            =    2.265286 (mean)
               t~ighconfi~e    =    .4998138 (mean)
               aibackground    =           1
               normalized~2    =    .5668233 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .202985   .0116236    17.46   0.000     .1802032    .2257669
          2  |   .2219471   .0063142    35.15   0.000     .2095715    .2343227
          3  |   .2365094   .0046572    50.78   0.000     .2273815    .2456373
          4  |   .2459345   .0063295    38.85   0.000     .2335289    .2583402
          5  |   .2497665   .0081213    30.75   0.000      .233849     .265684
          6  |   .2478251     .00999    24.81   0.000      .228245    .2674051
          7  |   .2402011   .0132067    18.19   0.000     .2143165    .2660858
          8  |   .2272593   .0184903    12.29   0.000      .191019    .2634997
          9  |   .2096445   .0254325     8.24   0.000     .1597977    .2594913
         10  |   .1882791   .0330691     5.69   0.000     .1234648    .2530934
         11  |   .1643329   .0402848     4.08   0.000     .0853762    .2432896
------------------------------------------------------------------------------

. 
. coefplot m4, vertical recast(line) lcolor("116 143 70") lwidth(*2) ciopts(fcolor("206 209 175") finte
> nsity(50) recast(rarea) lpatt(dash) lcolor(none)) xtitle(" " "AI Background Index Squared", size(meds
> mall)) ytitle("Predicted Probability of Switching (AI Treatment)" " ", size(medsmall)) xlabel(1 "0" 2
>  ".1" 3 ".2" 4 ".3" 5 ".4" 6 ".5" 7 ".6" 8 ".7" 9 ".8" 10 ".9" 11 "1", grid) title("AI Background Ind
> ex")

. 
. graph save "aibackgroundpredictedprobabilities.gph", replace
(file aibackgroundpredictedprobabilities.gph saved)

. graph export "aibackgroundpredictedprobabilities.tif", replace as(tif)
(file aibackgroundpredictedprobabilities.tif written in TIFF format)

. 
. * Robustness Regression Table in Appendix
. 
. estimates clear

. 
. * Country Variables for Models 4-7
. 
. *AI Familiarity Squared
. eststo m1: logit switch qpolitical level total_practice_correct age female educ treatment_highconfide
> nce overallai_familiarity aifamiliaritysquared normalized_overall_aibeliefs2 russia china uk australi
> a japan skorea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)

note: russia omitted because of collinearity
Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14084.545  
Iteration 2:   log pseudolikelihood = -14081.404  
Iteration 3:   log pseudolikelihood = -14081.403  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(17)     =     198.08
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14081.403               Pseudo R2         =     0.0180

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0213211    .009479     2.25   0.024     .0027426    .0398997
                        level |   .2015094   .0341199     5.91   0.000     .1346357    .2683831
       total_practice_correct |   -.028459   .0200687    -1.42   0.156     -.067793    .0108749
                          age |  -.0008027   .0015218    -0.53   0.598    -.0037853      .00218
                       female |   .2105997   .0475191     4.43   0.000     .1174639    .3037355
                         educ |   .0165769   .0153728     1.08   0.281    -.0135532    .0467069
     treatment_highconfidence |   .0884031   .0344652     2.56   0.010     .0208526    .1559536
        overallai_familiarity |   1.497472   .3773387     3.97   0.000     .7579013    2.237042
         aifamiliaritysquared |  -1.698239   .4601827    -3.69   0.000    -2.600181   -.7962977
normalized_overall_aibeliefs2 |  -.3367892   .1640743    -2.05   0.040    -.6583689   -.0152095
                       russia |          0  (omitted)
                        china |   .5837737     .10023     5.82   0.000     .3873265    .7802209
                           uk |   .2414593      .0838     2.88   0.004     .0772143    .4057043
                    australia |   .1973637   .0853573     2.31   0.021     .0300665    .3646609
                        japan |  -.0736904   .1081172    -0.68   0.496    -.2855962    .1382153
                       skorea |  -.1356818   .0994455    -1.36   0.172    -.3305913    .0592278
                       france |   .0428862   .0909219     0.47   0.637    -.1353174    .2210898
                       sweden |   -.008904   .0915925    -0.10   0.923     -.188422     .170614
                        _cons |  -1.583826   .1774584    -8.93   0.000    -1.931638   -1.236013
-----------------------------------------------------------------------------------------------

. 
. * AI Knowledge Squared
. eststo m2: logit switch qpolitical level total_practice_correct age female educ treatment_high overal
> lai_knowledge2 overallai_knowledge2squared normalized_overall_aibeliefs2 russia china uk australia ja
> pan skorea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)

note: russia omitted because of collinearity
Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14100.516  
Iteration 2:   log pseudolikelihood = -14097.614  
Iteration 3:   log pseudolikelihood = -14097.614  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(17)     =     187.67
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14097.614               Pseudo R2         =     0.0168

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0235255   .0095942     2.45   0.014     .0047212    .0423298
                        level |   .2015102   .0340518     5.92   0.000     .1347699    .2682504
       total_practice_correct |  -.0289047   .0201475    -1.43   0.151     -.068393    .0105836
                          age |  -.0010877   .0015194    -0.72   0.474    -.0040657    .0018903
                       female |   .1996227   .0472659     4.22   0.000     .1069832    .2922622
                         educ |   .0256364   .0150469     1.70   0.088    -.0038549    .0551277
     treatment_highconfidence |   .0873293   .0344481     2.54   0.011     .0198123    .1548463
         overallai_knowledge2 |   .5416588   .3894427     1.39   0.164    -.2216348    1.304953
  overallai_knowledge2squared |  -.8340461   .5045371    -1.65   0.098    -1.822921    .1548285
normalized_overall_aibeliefs2 |  -.2742694    .165929    -1.65   0.098    -.5994842    .0509455
                       russia |          0  (omitted)
                        china |   .6469974   .0977086     6.62   0.000      .455492    .8385027
                           uk |   .2498677   .0834911     2.99   0.003     .0862283    .4135072
                    australia |   .2106765   .0851046     2.48   0.013     .0438746    .3774783
                        japan |  -.0461009   .1086681    -0.42   0.671    -.2590864    .1668846
                       skorea |   -.088362   .1006905    -0.88   0.380    -.2857119    .1089878
                       france |   .0442244   .0909199     0.49   0.627    -.1339753    .2224242
                       sweden |  -.0014438   .0914156    -0.02   0.987    -.1806152    .1777275
                        _cons |  -1.537788   .1811388    -8.49   0.000    -1.892814   -1.182763
-----------------------------------------------------------------------------------------------

. 
. * AI Experience Squared
. eststo m3: logit switch qpolitical level total_practice_correct age female educ treatment_high overal
> lai_experience2 overallai_experience2squared normalized_overall_aibeliefs2 russia china uk australia 
> japan skorea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)

note: russia omitted because of collinearity
Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14076.101  
Iteration 2:   log pseudolikelihood = -14072.836  
Iteration 3:   log pseudolikelihood = -14072.835  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(17)     =     196.89
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14072.835               Pseudo R2         =     0.0186

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0282435    .009597     2.94   0.003     .0094338    .0470532
                        level |   .2025986    .034084     5.94   0.000     .1357952     .269402
       total_practice_correct |  -.0304708   .0200841    -1.52   0.129    -.0698349    .0088933
                          age |  -.0012288   .0016018    -0.77   0.443    -.0043683    .0019108
                       female |   .1881543   .0473539     3.97   0.000     .0953423    .2809663
                         educ |   .0313542   .0155046     2.02   0.043     .0009659    .0617426
     treatment_highconfidence |   .0873724   .0345112     2.53   0.011     .0197318    .1550131
        overallai_experience2 |   .9450776   .2690659     3.51   0.000     .4177181    1.472437
 overallai_experience2squared |  -1.383662   .2994237    -4.62   0.000    -1.970522   -.7968025
normalized_overall_aibeliefs2 |  -.2728617   .1668793    -1.64   0.102    -.5999392    .0542157
                       russia |          0  (omitted)
                        china |   .6889489    .098359     7.00   0.000     .4961688    .8817289
                           uk |   .2844191   .0836809     3.40   0.001     .1204075    .4484307
                    australia |   .2315405   .0852194     2.72   0.007     .0645135    .3985675
                        japan |  -.0478247    .108308    -0.44   0.659    -.2601045    .1644552
                       skorea |  -.0990312   .0998291    -0.99   0.321    -.2946926    .0966302
                       france |   .0431272   .0908066     0.47   0.635    -.1348505    .2211049
                       sweden |   .0008273   .0910069     0.01   0.993    -.1775431    .1791976
                        _cons |   -1.59598   .1791144    -8.91   0.000    -1.947038   -1.244922
-----------------------------------------------------------------------------------------------

. 
. *AI Background Squared
. eststo m4: logit switch qpolitical level total_practice_correct age female educ treatment_highconfide
> nce aibackground aibackgroundsquared normalized_overall_aibeliefs2 russia china uk australia japan sk
> orea france sweden[pweight=weight] if treatment_ai==1, cluster(caseid)

note: russia omitted because of collinearity
Iteration 0:   log pseudolikelihood = -14339.131  
Iteration 1:   log pseudolikelihood = -14093.689  
Iteration 2:   log pseudolikelihood = -14090.653  
Iteration 3:   log pseudolikelihood = -14090.653  

Logistic regression                             Number of obs     =     26,214
                                                Wald chi2(17)     =     188.99
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -14090.653               Pseudo R2         =     0.0173

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0238139   .0095105     2.50   0.012     .0051736    .0424541
                        level |   .2016976    .034057     5.92   0.000     .1349471    .2684481
       total_practice_correct |  -.0287761   .0201123    -1.43   0.152    -.0681955    .0106432
                          age |  -.0010807   .0016083    -0.67   0.502    -.0042328    .0020714
                       female |   .1972385   .0478719     4.12   0.000     .1034113    .2910657
                         educ |   .0273745   .0155381     1.76   0.078    -.0030796    .0578286
     treatment_highconfidence |   .0874671   .0344822     2.54   0.011     .0198832     .155051
                 aibackground |   1.181767   .4623141     2.56   0.011     .2756484    2.087886
          aibackgroundsquared |   -2.04073    .652971    -3.13   0.002     -3.32053   -.7609306
normalized_overall_aibeliefs2 |  -.2833429   .1669893    -1.70   0.090    -.6106359    .0439502
                       russia |          0  (omitted)
                        china |   .6655658   .0991871     6.71   0.000     .4711626    .8599689
                           uk |    .257933   .0835992     3.09   0.002     .0940816    .4217844
                    australia |   .2160372    .085143     2.54   0.011       .04916    .3829144
                        japan |   -.052135   .1082273    -0.48   0.630    -.2642566    .1599866
                       skorea |  -.1085947   .0997482    -1.09   0.276    -.3040976    .0869082
                       france |   .0368087   .0908171     0.41   0.685    -.1411897     .214807
                       sweden |  -.0030302   .0911852    -0.03   0.973    -.1817498    .1756895
                        _cons |  -1.607972   .1859841    -8.65   0.000    -1.972494    -1.24345
-----------------------------------------------------------------------------------------------

. 
. * Regression + Country Variables for Models 4-7
. 
. *AI Familiarity Squared
. eststo m5: regress switch qpolitical level total_practice_correct age female educ treatment_highconfi
> dence overallai_familiarity aifamiliaritysquared normalized_overall_aibeliefs2 russia china uk austra
> lia japan skorea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 26,176.8930462748)
note: russia omitted because of collinearity

Linear regression                               Number of obs     =     26,214
                                                F(17, 5912)       =      11.20
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0202
                                                Root MSE          =     .42116

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0037846   .0016728     2.26   0.024     .0005053    .0070638
                        level |   .0357906   .0060294     5.94   0.000     .0239707    .0476105
       total_practice_correct |  -.0049248   .0035133    -1.40   0.161    -.0118122    .0019626
                          age |  -.0001169   .0002737    -0.43   0.669    -.0006535    .0004197
                       female |   .0376146   .0085388     4.41   0.000     .0208754    .0543537
                         educ |   .0029484   .0027039     1.09   0.276    -.0023522    .0082491
     treatment_highconfidence |   .0157913   .0061149     2.58   0.010     .0038039    .0277787
        overallai_familiarity |     .27072   .0674333     4.01   0.000      .138526    .4029139
         aifamiliaritysquared |  -.3050832    .079691    -3.83   0.000    -.4613066   -.1488599
normalized_overall_aibeliefs2 |  -.0588549   .0287317    -2.05   0.041    -.1151795   -.0025302
                       russia |          0  (omitted)
                        china |   .1156597   .0194007     5.96   0.000     .0776272    .1536921
                           uk |   .0425252   .0146544     2.90   0.004     .0137971    .0712532
                    australia |    .034539   .0147957     2.33   0.020      .005534     .063544
                        japan |  -.0120408   .0173328    -0.69   0.487    -.0460194    .0219377
                       skorea |  -.0224526   .0158868    -1.41   0.158    -.0535965    .0086913
                       france |   .0071133   .0152119     0.47   0.640    -.0227076    .0369341
                       sweden |  -.0016307    .015126    -0.11   0.914    -.0312833    .0280218
                        _cons |   .1632853   .0310308     5.26   0.000     .1024537    .2241169
-----------------------------------------------------------------------------------------------

. 
. * AI Knowledge Squared
. eststo m6: regress switch qpolitical level total_practice_correct age female educ treatment_high over
> allai_knowledge2 overallai_knowledge2squared normalized_overall_aibeliefs2 russia china uk australia 
> japan skorea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 26,176.8930462748)
note: russia omitted because of collinearity

Linear regression                               Number of obs     =     26,214
                                                F(17, 5912)       =      10.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0190
                                                Root MSE          =     .42143

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0041477   .0016966     2.44   0.015     .0008217    .0074737
                        level |   .0358409   .0060263     5.95   0.000     .0240273    .0476546
       total_practice_correct |  -.0050262   .0035301    -1.42   0.155    -.0119464     .001894
                          age |   -.000178   .0002719    -0.65   0.513    -.0007111    .0003551
                       female |   .0354589   .0084798     4.18   0.000     .0188355    .0520824
                         educ |    .004644   .0026532     1.75   0.080    -.0005573    .0098453
     treatment_highconfidence |   .0156127   .0061189     2.55   0.011     .0036174     .027608
         overallai_knowledge2 |   .0946357   .0665114     1.42   0.155    -.0357509    .2250223
  overallai_knowledge2squared |  -.1445036   .0844895    -1.71   0.087     -.310134    .0211268
normalized_overall_aibeliefs2 |  -.0470653   .0289808    -1.62   0.104    -.1038784    .0097477
                       russia |          0  (omitted)
                        china |    .126998    .019087     6.65   0.000     .0895805    .1644155
                           uk |   .0440386   .0146226     3.01   0.003     .0153731    .0727041
                    australia |   .0367546   .0147791     2.49   0.013     .0077823     .065727
                        japan |  -.0071864   .0174551    -0.41   0.681    -.0414047    .0270319
                       skorea |   -.014264   .0161474    -0.88   0.377    -.0459188    .0173907
                       france |   .0071782   .0152251     0.47   0.637    -.0226686     .037025
                       sweden |    -.00023   .0151202    -0.02   0.988    -.0298712    .0294112
                        _cons |   .1720568   .0315743     5.45   0.000     .1101597    .2339538
-----------------------------------------------------------------------------------------------

. 
. * AI Experience Squared
. eststo m7: regress switch qpolitical level total_practice_correct age female educ treatment_high over
> allai_experience2 overallai_experience2squared normalized_overall_aibeliefs2 russia china uk australi
> a japan skorea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 26,176.8930462748)
note: russia omitted because of collinearity

Linear regression                               Number of obs     =     26,214
                                                F(17, 5912)       =      11.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0209
                                                Root MSE          =     .42102

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0050226    .001686     2.98   0.003     .0017174    .0083279
                        level |   .0359843     .00602     5.98   0.000      .024183    .0477856
       total_practice_correct |   -.005311   .0035146    -1.51   0.131    -.0122008    .0015788
                          age |  -.0002006   .0002878    -0.70   0.486    -.0007648    .0003635
                       female |   .0334732   .0084978     3.94   0.000     .0168144     .050132
                         educ |   .0055407   .0027254     2.03   0.042     .0001979    .0108835
     treatment_highconfidence |   .0156042   .0061157     2.55   0.011     .0036151    .0275933
        overallai_experience2 |   .1693171   .0470978     3.60   0.000     .0769882     .261646
 overallai_experience2squared |   -.246209   .0510347    -4.82   0.000    -.3462556   -.1461624
normalized_overall_aibeliefs2 |  -.0465817   .0291785    -1.60   0.110    -.1037822    .0106187
                       russia |          0  (omitted)
                        china |   .1346934   .0191614     7.03   0.000     .0971301    .1722568
                           uk |   .0505213   .0146516     3.45   0.001     .0217987    .0792438
                    australia |   .0406975   .0147888     2.75   0.006     .0117062    .0696889
                        japan |  -.0074827   .0173945    -0.43   0.667    -.0415824    .0266169
                       skorea |  -.0159952   .0159859    -1.00   0.317    -.0473335    .0153431
                       france |   .0072764   .0152037     0.48   0.632    -.0225285    .0370812
                       sweden |   .0003654   .0150394     0.02   0.981    -.0291173    .0298481
                        _cons |   .1614414   .0313426     5.15   0.000     .0999984    .2228844
-----------------------------------------------------------------------------------------------

. 
. *AI Background Squared
. eststo m8: regress switch qpolitical level total_practice_correct age female educ treatment_highconfi
> dence aibackground aibackgroundsquared normalized_overall_aibeliefs2 russia china uk australia japan 
> skorea france sweden[pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 26,176.8930462748)
note: russia omitted because of collinearity

Linear regression                               Number of obs     =     26,214
                                                F(17, 5912)       =      10.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0195
                                                Root MSE          =     .42131

                                              (Std. Err. adjusted for 5,913 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                   qpolitical |   .0042068   .0016755     2.51   0.012     .0009223    .0074913
                        level |   .0358713   .0060234     5.96   0.000     .0240632    .0476794
       total_practice_correct |  -.0050123   .0035207    -1.42   0.155    -.0119142    .0018896
                          age |  -.0001713   .0002895    -0.59   0.554    -.0007388    .0003961
                       female |   .0350556   .0086123     4.07   0.000     .0181722    .0519389
                         educ |   .0049109   .0027384     1.79   0.073    -.0004574    .0102791
     treatment_highconfidence |   .0156337   .0061201     2.55   0.011      .003636    .0276314
                 aibackground |   .2143425   .0799592     2.68   0.007     .0575932    .3710917
          aibackgroundsquared |  -.3691189     .11114    -3.32   0.001    -.5869939    -.151244
normalized_overall_aibeliefs2 |  -.0491018    .029249    -1.68   0.093    -.1064406    .0082369
                       russia |          0  (omitted)
                        china |   .1306099   .0193244     6.76   0.000      .092727    .1684929
                           uk |   .0455088    .014636     3.11   0.002     .0168169    .0742007
                    australia |   .0378302   .0147793     2.56   0.011     .0088574     .066803
                        japan |  -.0082514   .0173832    -0.47   0.635    -.0423289    .0258261
                       skorea |  -.0177813   .0159907    -1.11   0.266     -.049129    .0135664
                       france |   .0059369   .0152048     0.39   0.696    -.0238702    .0357439
                       sweden |  -.0005758   .0150755    -0.04   0.970    -.0301293    .0289776
                        _cons |   .1591258   .0325417     4.89   0.000     .0953321    .2229194
-----------------------------------------------------------------------------------------------

. 
. esttab m1 m2 m3 m4 m5 m6 m7 m8 using TableAppendix.tex, replace f t(3) scalars("ll Log Likelihood" F)
>  legend label collabels(none) varlabels(_cons Constant) se(3) pr2 r2 b(3) star(* 0.10 ** 0.05 *** 0.0
> 1) nobaselevels order(qpolitical level total_practice_correct age female educ treatment_ai treatment_
> highconfidence overallai_familiarity aifamiliaritysquared overallai_knowledge2 overallai_knowledge2sq
> uared overallai_experience2 overallai_experience2squared aibackground aibackgroundsquared normalized_
> overall_aibeliefs2) drop(treatment_ai russia china uk australia japan skorea france sweden) mtitles("
> \shortstack{AI Familiarity\\Country Vars\\b/SE}" "\shortstack{AI Knowledge\\Country Vars\\b/SE}" "\sh
> ortstack{AI Experience\\Country Vars\\b/SE}" "\shortstack{AI Background\\Country Vars\\b/SE}" "\short
> stack{AI Familiarity\\OLS\\b/SE}" "\shortstack{AI Knowledge\\OLS\\b/SE}" "\shortstack{AI Experience\\
> OLS\\b/SE}" "\shortstack{AI Background\\OLS\\b/SE}") eqlabel(none)
(output written to TableAppendix.tex)

. 
. estimates clear

. 
. * Robustness Regression Table #2 in Appendix (no political variable so Russia included)
. 
. * Country Variables for Models 4-7
. 
. *AI Familiarity Squared
. eststo m1: logit switch level total_practice_correct age female educ treatment_highconfidence overall
> ai_familiarity aifamiliaritysquared normalized_overall_aibeliefs2 russia china uk australia japan sko
> rea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -17664.226  
Iteration 1:   log pseudolikelihood = -17379.964  
Iteration 2:   log pseudolikelihood = -17376.033  
Iteration 3:   log pseudolikelihood = -17376.033  

Logistic regression                             Number of obs     =     32,695
                                                Wald chi2(17)     =     213.26
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -17376.033               Pseudo R2         =     0.0163

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |   .1869078   .0308298     6.06   0.000     .1264824    .2473331
       total_practice_correct |  -.0400299   .0176356    -2.27   0.023     -.074595   -.0054647
                          age |  -.0006037   .0013751    -0.44   0.661    -.0032989    .0020916
                       female |   .2197551   .0430598     5.10   0.000     .1353594    .3041507
                         educ |   .0125082   .0143212     0.87   0.382    -.0155607    .0405772
     treatment_highconfidence |   .0681122   .0311927     2.18   0.029     .0069757    .1292487
        overallai_familiarity |   1.201359   .3624668     3.31   0.001      .490937    1.911781
         aifamiliaritysquared |  -1.475223   .4468082    -3.30   0.001    -2.350951   -.5994948
normalized_overall_aibeliefs2 |  -.2978746   .1460691    -2.04   0.041    -.5841647   -.0115845
                       russia |  -.1166384   .0952548    -1.22   0.221    -.3033344    .0700576
                        china |   .6034185   .0985467     6.12   0.000     .4102705    .7965665
                           uk |   .2301889   .0786536     2.93   0.003     .0760307     .384347
                    australia |   .1644836   .0800409     2.05   0.040     .0076064    .3213608
                        japan |  -.1284858   .0979209    -1.31   0.189    -.3204072    .0634356
                       skorea |  -.1527497    .093941    -1.63   0.104    -.3368706    .0313712
                       france |   .0089681   .0850134     0.11   0.916    -.1576551    .1755913
                       sweden |  -.0113937   .0868004    -0.13   0.896    -.1815194     .158732
                        _cons |  -1.378743   .1413225    -9.76   0.000     -1.65573   -1.101756
-----------------------------------------------------------------------------------------------

. 
. * AI Knowledge Squared
. eststo m2: logit switch level total_practice_correct age female educ treatment_high overallai_knowled
> ge2 overallai_knowledge2squared normalized_overall_aibeliefs2 russia china uk australia japan skorea 
> france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -17664.226  
Iteration 1:   log pseudolikelihood = -17390.678  
Iteration 2:   log pseudolikelihood = -17386.908  
Iteration 3:   log pseudolikelihood = -17386.907  

Logistic regression                             Number of obs     =     32,695
                                                Wald chi2(17)     =     203.47
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -17386.907               Pseudo R2         =     0.0157

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |   .1870149   .0307866     6.07   0.000     .1266744    .2473555
       total_practice_correct |  -.0407139   .0176781    -2.30   0.021    -.0753624   -.0060654
                          age |  -.0008049   .0013692    -0.59   0.557    -.0034884    .0018786
                       female |   .2117113    .042749     4.95   0.000     .1279249    .2954977
                         educ |    .017983   .0140191     1.28   0.200     -.009494      .04546
     treatment_highconfidence |   .0667828   .0311588     2.14   0.032     .0057126     .127853
         overallai_knowledge2 |   .5310124   .3660883     1.45   0.147    -.1865076    1.248532
  overallai_knowledge2squared |  -.8130811   .4682937    -1.74   0.083     -1.73092    .1047577
normalized_overall_aibeliefs2 |  -.2562826   .1472511    -1.74   0.082    -.5448895    .0323243
                       russia |  -.1043428   .0946756    -1.10   0.270    -.2899035    .0812179
                        china |    .644908   .0951173     6.78   0.000     .4584815    .8313345
                           uk |   .2360583   .0783941     3.01   0.003     .0824088    .3897078
                    australia |   .1716013   .0799165     2.15   0.032     .0149678    .3282347
                        japan |  -.1156085   .0984865    -1.17   0.240    -.3086386    .0774215
                       skorea |  -.1155416   .0946778    -1.22   0.222    -.3011066    .0700234
                       france |   .0102431   .0850629     0.12   0.904     -.156477    .1769633
                       sweden |  -.0065374   .0866891    -0.08   0.940    -.1764448    .1633701
                        _cons |  -1.338487   .1432629    -9.34   0.000    -1.619277   -1.057697
-----------------------------------------------------------------------------------------------

. 
. * AI Experience Squared
. eststo m3: logit switch level total_practice_correct age female educ treatment_high overallai_experie
> nce2 overallai_experience2squared normalized_overall_aibeliefs2 russia china uk australia japan skore
> a france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -17664.226  
Iteration 1:   log pseudolikelihood = -17372.356  
Iteration 2:   log pseudolikelihood = -17368.319  
Iteration 3:   log pseudolikelihood = -17368.318  

Logistic regression                             Number of obs     =     32,695
                                                Wald chi2(17)     =     209.50
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -17368.318               Pseudo R2         =     0.0168

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |   .1872441   .0307828     6.08   0.000     .1269109    .2475774
       total_practice_correct |  -.0418783    .017619    -2.38   0.017     -.076411   -.0073456
                          age |  -.0009377   .0014369    -0.65   0.514    -.0037539    .0018786
                       female |   .1987543   .0430169     4.62   0.000     .1144427    .2830659
                         educ |   .0238116   .0144427     1.65   0.099    -.0044957    .0521188
     treatment_highconfidence |   .0672312   .0312123     2.15   0.031     .0060562    .1284062
        overallai_experience2 |   .7491742   .2562374     2.92   0.003     .2469581     1.25139
 overallai_experience2squared |   -1.13648   .2893277    -3.93   0.000    -1.703552   -.5694081
normalized_overall_aibeliefs2 |  -.2425352   .1488191    -1.63   0.103    -.5342152    .0491448
                       russia |   -.122605   .0949822    -1.29   0.197    -.3087667    .0635567
                        china |   .6827059   .0959835     7.11   0.000     .4945817    .8708301
                           uk |   .2612257   .0785121     3.33   0.001     .1073447    .4151066
                    australia |   .1837111    .079943     2.30   0.022     .0270256    .3403966
                        japan |  -.1240613   .0983031    -1.26   0.207    -.3167318    .0686093
                       skorea |  -.1301249   .0940175    -1.38   0.166    -.3143957     .054146
                       france |   .0053436   .0848892     0.06   0.950    -.1610362    .1717234
                       sweden |  -.0057938   .0864216    -0.07   0.947    -.1751771    .1635895
                        _cons |  -1.351848   .1429184    -9.46   0.000    -1.631963   -1.071733
-----------------------------------------------------------------------------------------------

. 
. *AI Background Squared
. eststo m4: logit switch level total_practice_correct age female educ treatment_highconfidence aibackg
> round aibackgroundsquared normalized_overall_aibeliefs2 russia china uk australia japan skorea france
>  sweden[pweight=weight] if treatment_ai==1, cluster(caseid)

Iteration 0:   log pseudolikelihood = -17664.226  
Iteration 1:   log pseudolikelihood =  -17385.71  
Iteration 2:   log pseudolikelihood = -17381.837  
Iteration 3:   log pseudolikelihood = -17381.836  

Logistic regression                             Number of obs     =     32,695
                                                Wald chi2(17)     =     205.60
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -17381.836               Pseudo R2         =     0.0160

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |   .1871236   .0307758     6.08   0.000     .1268041     .247443
       total_practice_correct |  -.0403401   .0176475    -2.29   0.022    -.0749285   -.0057517
                          age |  -.0008648   .0014359    -0.60   0.547    -.0036791    .0019495
                       female |   .2067785   .0433054     4.77   0.000     .1219015    .2916555
                         educ |   .0211461   .0145358     1.45   0.146    -.0073436    .0496359
     treatment_highconfidence |   .0672727   .0311977     2.16   0.031     .0061262    .1284191
                 aibackground |   .9416361   .4516404     2.08   0.037     .0564372    1.826835
          aibackgroundsquared |  -1.756329   .6387452    -2.75   0.006    -3.008246   -.5044113
normalized_overall_aibeliefs2 |  -.2452784   .1488204    -1.65   0.099     -.536961    .0464042
                       russia |  -.1244187   .0952198    -1.31   0.191    -.3110461    .0622087
                        china |   .6685938   .0974125     6.86   0.000     .4776688    .8595187
                           uk |    .242434   .0784655     3.09   0.002     .0886444    .3962235
                    australia |   .1760065    .079895     2.20   0.028     .0194152    .3325978
                        japan |   -.120792   .0982527    -1.23   0.219    -.3133638    .0717798
                       skorea |  -.1321786   .0940575    -1.41   0.160     -.316528    .0521707
                       france |   .0029568   .0849551     0.03   0.972     -.163552    .1694657
                       sweden |  -.0076528   .0865436    -0.09   0.930     -.177275    .1619695
                        _cons |  -1.383716   .1489824    -9.29   0.000    -1.675716   -1.091715
-----------------------------------------------------------------------------------------------

. 
. * Regression + Country Variables for Models 4-7
. 
. *AI Familiarity Squared
. eststo m5: regress switch level total_practice_correct age female educ treatment_highconfidence overa
> llai_familiarity aifamiliaritysquared normalized_overall_aibeliefs2 russia china uk australia japan s
> korea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 32,581.8935777543)

Linear regression                               Number of obs     =     32,695
                                                F(17, 7379)       =      12.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0183
                                                Root MSE          =     .41859

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |   .0327795   .0053908     6.08   0.000      .022212     .043347
       total_practice_correct |  -.0068854   .0030447    -2.26   0.024    -.0128538    -.000917
                          age |  -.0000907   .0002438    -0.37   0.710    -.0005686    .0003872
                       female |   .0385732    .007588     5.08   0.000     .0236986    .0534478
                         educ |   .0021506    .002474     0.87   0.385    -.0026992    .0070003
     treatment_highconfidence |   .0120025   .0054636     2.20   0.028     .0012922    .0227127
        overallai_familiarity |   .2133017   .0635081     3.36   0.001     .0888077    .3377956
         aifamiliaritysquared |  -.2608599   .0760453    -3.43   0.001    -.4099305   -.1117894
normalized_overall_aibeliefs2 |  -.0518367   .0253677    -2.04   0.041    -.1015647   -.0021087
                       russia |  -.0189751   .0152915    -1.24   0.215    -.0489508    .0110007
                        china |   .1203925    .019351     6.22   0.000     .0824591    .1583259
                           uk |   .0411024   .0138398     2.97   0.003     .0139724    .0682323
                    australia |   .0289594   .0139202     2.08   0.038     .0016717     .056247
                        japan |  -.0210375   .0156847    -1.34   0.180    -.0517841    .0097091
                       skorea |  -.0252773   .0150943    -1.67   0.094    -.0548664    .0043118
                       france |   .0012633    .014277     0.09   0.929    -.0267236    .0292502
                       sweden |  -.0021892   .0144856    -0.15   0.880    -.0305851    .0262066
                        _cons |   .2004843   .0242747     8.26   0.000      .152899    .2480697
-----------------------------------------------------------------------------------------------

. 
. * AI Knowledge Squared
. eststo m6: regress switch level total_practice_correct age female educ treatment_high overallai_knowl
> edge2 overallai_knowledge2squared normalized_overall_aibeliefs2 russia china uk australia japan skore
> a france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 32,581.8935777543)

Linear regression                               Number of obs     =     32,695
                                                F(17, 7379)       =      11.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0176
                                                Root MSE          =     .41873

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |    .032829    .005387     6.09   0.000     .0222689    .0433892
       total_practice_correct |  -.0070148   .0030518    -2.30   0.022    -.0129971   -.0010324
                          age |  -.0001325   .0002418    -0.55   0.584    -.0006065    .0003416
                       female |   .0371273   .0075239     4.93   0.000     .0223783    .0518762
                         educ |   .0031586    .002423     1.30   0.192    -.0015911    .0079083
     treatment_highconfidence |   .0117715   .0054609     2.16   0.031     .0010665    .0224765
         overallai_knowledge2 |   .0904455   .0618419     1.46   0.144    -.0307823    .2116733
  overallai_knowledge2squared |  -.1372826   .0771929    -1.78   0.075    -.2886028    .0140376
normalized_overall_aibeliefs2 |  -.0439488   .0254833    -1.72   0.085    -.0939033    .0060058
                       russia |  -.0169755    .015195    -1.12   0.264    -.0467621    .0128111
                        china |   .1276336   .0188395     6.77   0.000     .0907028    .1645645
                           uk |   .0421379   .0138073     3.05   0.002     .0150716    .0692042
                    australia |   .0301237   .0139181     2.16   0.030     .0028403    .0574071
                        japan |  -.0188407   .0157932    -1.19   0.233    -.0497998    .0121184
                       skorea |  -.0189762   .0152454    -1.24   0.213    -.0488614    .0109091
                       france |   .0013985   .0142912     0.10   0.922    -.0266164    .0294134
                       sweden |  -.0012814   .0144795    -0.09   0.929    -.0296655    .0271026
                        _cons |   .2077659   .0245749     8.45   0.000      .159592    .2559397
-----------------------------------------------------------------------------------------------

. 
. * AI Experience Squared
. eststo m7: regress switch level total_practice_correct age female educ treatment_high overallai_exper
> ience2 overallai_experience2squared normalized_overall_aibeliefs2 russia china uk australia japan sko
> rea france sweden [pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 32,581.8935777543)

Linear regression                               Number of obs     =     32,695
                                                F(17, 7379)       =      11.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0188
                                                Root MSE          =     .41849

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |    .032795   .0053805     6.10   0.000     .0222476    .0433424
       total_practice_correct |  -.0072547   .0030445    -2.38   0.017    -.0132227   -.0012867
                          age |  -.0001559   .0002547    -0.61   0.541    -.0006551    .0003434
                       female |   .0348219   .0075765     4.60   0.000     .0199698    .0496739
                         educ |    .004137   .0024938     1.66   0.097    -.0007515    .0090254
     treatment_highconfidence |   .0118427   .0054624     2.17   0.030     .0011348    .0225506
        overallai_experience2 |    .131265   .0443692     2.96   0.003     .0442887    .2182412
 overallai_experience2squared |   -.198166   .0486751    -4.07   0.000     -.293583   -.1027489
normalized_overall_aibeliefs2 |  -.0413346   .0258118    -1.60   0.109    -.0919331    .0092638
                       russia |  -.0199835   .0152503    -1.31   0.190    -.0498785    .0099114
                        china |   .1343938   .0189606     7.09   0.000     .0972257    .1715619
                           uk |    .046933    .013832     3.39   0.001     .0198183    .0740477
                    australia |   .0324517   .0139205     2.33   0.020     .0051637    .0597398
                        japan |   -.020395   .0157678    -1.29   0.196    -.0513044    .0105143
                       skorea |  -.0213749   .0151304    -1.41   0.158    -.0510348    .0082849
                       france |   .0006658   .0142631     0.05   0.963    -.0272939    .0286255
                       sweden |  -.0010584    .014432    -0.07   0.942    -.0293493    .0272325
                        _cons |   .2054591   .0245692     8.36   0.000     .1572965    .2536217
-----------------------------------------------------------------------------------------------

. 
. *AI Background Squared
. eststo m8: regress switch level total_practice_correct age female educ treatment_highconfidence aibac
> kground aibackgroundsquared normalized_overall_aibeliefs2 russia china uk australia japan skorea fran
> ce sweden[pweight=weight] if treatment_ai==1, cluster(caseid)
(sum of wgt is 32,581.8935777543)

Linear regression                               Number of obs     =     32,695
                                                F(17, 7379)       =      11.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0180
                                                Root MSE          =     .41866

                                              (Std. Err. adjusted for 7,380 clusters in caseid)
-----------------------------------------------------------------------------------------------
                              |               Robust
                       switch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                        level |   .0328267   .0053836     6.10   0.000     .0222733      .04338
       total_practice_correct |  -.0069682   .0030475    -2.29   0.022    -.0129422   -.0009942
                          age |  -.0001401   .0002548    -0.55   0.582    -.0006395    .0003592
                       female |    .036226   .0076349     4.74   0.000     .0212594    .0511926
                         educ |   .0037052   .0025132     1.47   0.140    -.0012213    .0086317
     treatment_highconfidence |   .0118468   .0054647     2.17   0.030     .0011344    .0225592
                 aibackground |   .1691255   .0779821     2.17   0.030     .0162583    .3219927
          aibackgroundsquared |  -.3149493   .1085947    -2.90   0.004    -.5278259   -.1020727
normalized_overall_aibeliefs2 |  -.0422652   .0258363    -1.64   0.102    -.0929117    .0083813
                       russia |  -.0204486   .0152982    -1.34   0.181    -.0504374    .0095402
                        china |   .1320786   .0192244     6.87   0.000     .0943933    .1697639
                           uk |   .0433888   .0138202     3.14   0.002     .0162973    .0704803
                    australia |   .0310355   .0139113     2.23   0.026     .0037654    .0583056
                        japan |  -.0197603   .0157597    -1.25   0.210    -.0506539    .0111332
                       skorea |  -.0217732   .0151484    -1.44   0.151    -.0514684     .007922
                       france |   .0001238   .0142737     0.01   0.993    -.0278567    .0281043
                       sweden |  -.0015618   .0144547    -0.11   0.914    -.0298971    .0267735
                        _cons |   .1994132   .0256655     7.77   0.000     .1491015    .2497249
-----------------------------------------------------------------------------------------------

. 
. esttab m1 m2 m3 m4 m5 m6 m7 m8 using TableAppendix2.tex, replace f t(3) scalars("ll Log Likelihood" F
> ) legend label collabels(none) varlabels(_cons Constant) se(3) pr2 r2 b(3) star(* 0.10 ** 0.05 *** 0.
> 01) nobaselevels order(level total_practice_correct age female educ treatment_ai treatment_highconfid
> ence overallai_familiarity aifamiliaritysquared overallai_knowledge2 overallai_knowledge2squared over
> allai_experience2 overallai_experience2squared aibackground aibackgroundsquared normalized_overall_ai
> beliefs2) drop(treatment_ai russia china uk australia japan skorea france sweden) mtitles("\shortstac
> k{AI Familiarity\\Country Vars\\b/SE}" "\shortstack{AI Knowledge\\Country Vars\\b/SE}" "\shortstack{A
> I Experience\\Country Vars\\b/SE}" "\shortstack{AI Background\\Country Vars\\b/SE}" "\shortstack{AI F
> amiliarity\\OLS\\b/SE}" "\shortstack{AI Knowledge\\OLS\\b/SE}" "\shortstack{AI Experience\\OLS\\b/SE}
> " "\shortstack{AI Background\\OLS\\b/SE}") eqlabel(none)
(output written to TableAppendix2.tex)

. 
. log close
      name:  <unnamed>
       log:  C:\Users\mhoro\Dropbox\Automation Bias Survey Data\HorowitzKahnAutomationBiasReplication.l
> og
  log type:  text
 closed on:  14 Jan 2024, 14:45:57
-------------------------------------------------------------------------------------------------------
